Protein Structure Prediction With Improved Quantum Immune Algorihm

Prediction of protein folding structure is a key issue in bioinformatics, which helps to explain a variety of biological phenomena, and can be used to predict or control of biological phenomena. In order to simulate the process of protein folding many simplified models had been proposed. One of the widely used models is the AB off-lattice model.The clonal selection algorithm (CSA) that is one of the most representative Immune Algorithms(IA), which has strong ability in local optimum. To be a novel algorithm , Quantum Algorithms have high efficiency in search the solutions. The Clonal Selection Algorithm was introduced into the hypermutation operators in the Quantum Algorithm to improve the local search ability, and double chains quantum coded was designed to enlarge the probability of the global optimization solution. It showed that the solution mostly trap into the local optimum, to escape the local best solution the aging operator is introduced to improve the performance of the algorithm.Experimental results showed that the lowest energies and computing-time of the improved Quantum Clonal Selection(QCS) algorithm were better than that of the previous methods, and the QCS was further improved by adding aging operator to combat the premature convergence. Compared with previous approaches, the improved QCS algorithm remarkably enhanced the convergence performance and the search efficiency of the immune optimization algorithm.